# Deep Convolutional Neural Network and Sparse Least Squares Migration

## TL;DR

This paper links deep CNNs to seismic migration, proposing a neural network version of sparse least squares migration that reduces computation and enhances noise removal for high-resolution imaging.

## Contribution

It introduces neural network sparse least squares migration (NNLSM), connecting CNN filters to seismic Green's functions, and offers a computationally efficient, noise-robust imaging method.

## Key findings

- NNLSM reduces computational cost compared to standard LSM.
- NNLSM effectively denoises seismic migration images.
- Quasi-reflectivity images serve as superresolution attributes.

## Abstract

We recast the forward pass of a multilayered convolutional neural network (CNN) as the solution to the problem of sparse least squares migration (LSM). The CNN filters and feature maps are shown to be analogous, but not equivalent, to the migration Green's functions and the quasi-reflectivity distribution, respectively. This provides a physical interpretation of the filters and feature maps in deep CNN in terms of the operators for seismic imaging. Motivated by the connection between sparse LSM and CNN, we propose the neural network version of sparse LSM. Unlike the standard LSM method that finds the optimal reflectivity image, neural network LSM (NNLSM) finds both the optimal quasi-reflectivity image and the quasi-migration Green's functions. These quasi-migration-Green's functions are also denoted as the convolutional filters in a CNN and are similar to migration Green's functions. The advantage of NNLSM over standard LSM is that its computational cost is significantly less and it can be used for denoising coherent and incoherent noise in migration images. Its disadvantage is that the NNLSM quasi-reflectivity image is only an approximation to the actual reflectivity distribution. However, the quasi-reflectivity image can be used as a superresolution attribute image for high-resolution delineation of geologic bodies.